Ubiquitous evaluation of layer potentials using Quadrature by Kernel-Independent Expansion

• Published in: BIT Vol. 58; no. 2; pp. 423 - 456
• Main Authors: Rahimian, Abtin, Barnett, Alex, Zorin, Denis
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.06.2018; Springer Nature B.V
• Subjects: Addition theorem; Algorithms; Computational Mathematics and Numerical Analysis; Elastostatics; Fluid dynamics; Kernels; Mathematical analysis; Mathematics; Mathematics and Statistics; Numeric Computing; Quadratures; Stokes law (fluid mechanics); Test procedures; Near-singular integrals; 65R20; Kernel-independent; High order quadrature; Elliptic Boundary value problem; Boundary integral equations
• ISSN: 0006-3835; 1572-9125
• DOI: 10.1007/s10543-017-0689-2

We introduce a quadrature scheme—QBKIX —for the ubiquitous high-order accurate evaluation of singular layer potentials associated with general elliptic PDEs, i.e., a scheme that yields high accuracy at all distances to the domain boundary as well as on the boundary itself. Relying solely on point evaluations of the underlying kernel, our scheme is essentially PDE-independent; in particular, no analytic expansion nor addition theorem is required. Moreover, it applies to boundary integrals with singular, weakly singular, and hypersingular kernels. Our work builds upon quadrature by expansion, which approximates the potential by an analytic expansion in the neighborhood of each expansion center. In contrast, we use a sum of fundamental solutions lying on a ring enclosing the neighborhood, and solve a small dense linear system for their coefficients to match the potential on a smaller concentric ring. We test the new method with Laplace, Helmholtz, Yukawa, Stokes, and Navier (elastostatic) kernels in two dimensions (2D) using adaptive, panel-based boundary quadratures on smooth and corner domains. Advantages of the algorithm include its relative simplicity of implementation, immediate extension to new kernels, dimension-independence (allowing simple generalization to 3D), and compatibility with fast algorithms such as the kernel-independent FMM.